A kind of four-wheel drive vehicle is provided with a driving force distribution device for distributing a driving force generated in a driving source such as an engine to main driving wheels and auxiliary driving wheels. In this kind of four-wheel drive vehicle in which the main driving wheels are front wheels and the auxiliary driving wheels are rear wheels, for example, the driving force generated in the driving source is transmitted to the front wheels via a front drive shaft and a front differential. The driving force is also transmitted to the driving force distribution device provided with a hydraulic clutch via a propeller shaft. Then, supplying hydraulic fluid under a predetermined pressure from the hydraulic control device to the driving force distribution device controls an engagement pressure of the hydraulic clutch included by the driving force distribution device. This allows the driving force of the driving source to transmit at a predetermined distribution ratio to the rear wheels that are the auxiliary driving wheels.
In the above-described hydraulic clutch of the driving force distribution device, a friction coefficient of the hydraulic clutch increases at a low temperature of the hydraulic fluid supplied to the hydraulic clutch. This makes torque transmitted to the rear wheels excessive. This torque might exceed a target strength of the hydraulic clutch. The hydraulic control device estimates a temperature of the hydraulic fluid so as to be lower than an actual temperature, and limits a controlled amount of the hydraulic clutch until the estimated temperature exceeds a specified temperature. In this way, the hydraulic control device prevents the torque exceeding the target strength from occurring to the hydraulic clutch. This necessitates estimation of the temperature of the hydraulic fluid supplied to the hydraulic clutch as accurately as possible.
Regarding this necessity, various estimation techniques and determination techniques have been conventionally proposed for measuring a physical quantity of the hydraulic fluid (oil). Among these techniques, preceding studies suggest effectiveness of a technique using machine learning. The machine learning technique is capable of model construction only with data desired to be modeled. Therefore, this technique has a merit to construct a model even without much knowledge and information about an internal structure of a target system. Balabin et al. propose techniques to discriminate between mineral oil and synthetic fluid in engine oil and determine viscosity of the engine oil. These techniques are acquired by applying identification methods such as the k-neighborhood method (k-NN), an artificial neural network (ANN) and a support vector machine (SVM) to absorption spectrum data achieved using the near-infrared spectroscopy. These techniques are disclosed in Non-patent document 1. Further, multiple studies report a technique for estimating a temperature of insulating oil in an oil-immersed transformer. This estimation uses the ANN that has been learned from a load of the transformer and an ambient temperature for estimating the temperature of the insulating oil in the oil-immersed transformer. This technique is disclosed in Non-patent documents 2 to 4. Similarly, another study covers the oil-immersed transformer. In this oil-immersed transformer, a gas composition contained in oil is used as a feature quantity to determine an abnormal state of an on-load tap switching device by applying a logistic regression and the ANN. This technique is disclosed in Non-patent document 5. This document states that an approach applying the ANN is more effective than an approach applying the logistic regression due to better determination accuracy of the ANN than that of the logistic regression.
However, each of the above-described studies proposes the technique that makes a determination using only a one-time output from a model. Therefore, any of these techniques may make an erroneous determination due to variations in a system operation at the time of determination or due to noises in measured data.
Meanwhile, Patent document 1 discloses a technique for detecting a state transition of a plunger position from coil currents. The aim of this technique is to estimate the plunger position in a linear solenoid. It should be noted that the linear solenoid here corresponds to a “proportion magnet,” and the state transition here corresponds to a transition between two states that are a “hold-in range” and a “control range” in Patent document 1.
Here, the “hold-in range” in Patent document 1 is a state in which the solenoid is stopped, and the “control range” is a state between a hold-in range and another hold-in range. In other words, the transition of “a state in which the solenoid is open at a target value→the plunger is in motion→a state in which the solenoid is open at another target value” is expressed as “a hold-in range→a control range→another hold-in range” in Patent document 1.
In the case of the transition from the hold-in range to the control range, a coil current rises for an instant immediately after the plunger starts to move. To this coil current that has risen, a threshold value is applied so as to set a next target current after the plunger starts to move. This enables the transition from the hold-in range to the other hold-in range. In other words, a low coil current prior to the plunger motion precludes setting a target coil current before an actual coil current rises.
However, since the technique disclosed in Patent document 1 also employs an algorithm that relies on only a single determination, a determination threshold value must be set at a value high enough to prevent an erroneous determination. Then, a determination of current rise is delayed, thereby possibly failing to control a plunger position if, for example, hold-in ranges before and after a transition are close to each other. On the contrary, a low determination threshold value increases possibility of the erroneous determination.
As described above, many conventional techniques for detecting oil properties have been proposed. However, many of them claim that a high-performance model with a large amount of calculation can ensure a highly accurate estimation and determination. Therefore, many of these techniques are difficult to be implemented to an on-vehicle unit that has moderate calculation capability but is required to process in real time.
In a system required to be highly reliable such as an on-vehicle system, reliability of a result derived from a determination and estimation algorithm is essential. However, almost all techniques including the above-described conventional ones have not been discussed in terms of reliability. In addition, one can conceive of a commonly used technique that relies on a determination and estimation result detected multiple times. However, this technique needs to examine how to quantitatively evaluate a reliable number of times for every object to be detected. Furthermore, the above-described techniques entail a trade-off, that is, “a higher determination threshold value increases reliability of a result, but decreases detection frequency,” and “a lower determination threshold value increases detection frequency, but decreases reliability of a result.” This requires to set a threshold value in consideration of such trade off.